| 1 |
From Spikes to Rates |
|
| 2 |
Perceptrons: Simple and Multilayer |
|
| 3 |
Perceptrons as Models of Vision |
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| 4 |
Linear Networks |
Problem set 1 due |
| 5 |
Retina |
|
| 6 |
Lateral Inhibition and Feature Selectivity |
Problem set 2 due |
| 7 |
Objectives and Optimization |
Problem set 3 due |
| 8 |
Hybrid Analog-Digital Computation
Ring Network |
|
| 9 |
Constraint Satisfaction
Stereopsis |
Problem set 4 due |
| 10 |
Bidirectional Perception |
|
| 11 |
Signal Reconstruction |
Problem set 5 due |
| 12 |
Hamiltonian Dynamics |
|
|
Midterm |
|
| 13 |
Antisymmetric Networks |
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| 14 |
Excitatory-Inhibitory Networks
Learning |
|
| 15 |
Associative Memory |
|
| 16 |
Models of Delay Activity
Integrators |
Problem set 6 due one day after Lec #16 |
| 17 |
Multistability
Clustering |
|
| 18 |
VQ
PCA |
Problem set 7 due |
| 19 |
More PCA
Delta Rule |
Problem set 8 due |
| 20 |
Conditioning
Backpropagation |
|
| 21 |
More Backpropagation |
Problem set 9 due |
| 22 |
Stochastic Gradient Descent |
|
| 23 |
Reinforcement Learning |
Problem set 10 due |
| 24 |
More Reinforcement Learning |
|
| 25 |
Final Review |
|
|
Final Exam |
|